Related Experiment Video
Updated: Apr 8, 2026

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
Nomogram for predicting pathologically complete response after neoadjuvant chemoradiotherapy for oesophageal cancer
Eelke Lucie Anne Toxopeus1, Daan Nieboer2, Joel Shapiro1
1Department of Surgery, Erasmus MC, University Medical Centre Rotterdam, The Netherlands.
Background:
A pathologically complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) is seen in 30% of the patients with oesophageal cancer. The aim is to identify patient and tumour characteristics associated with a pCR and to develop a nomogram for the prediction of pCR.
Patients And Methods:
Patients who underwent nCRT followed by surgery were identified and response to nCRT was assessed according to a modified Mandard classification in the resection specimen. A model was developed with age, gender, histology and location of the tumour, differentiation grade, alcohol use, smoking, percentage weight loss, Charlson Comorbidity Index (CCI), cT-stage and cN-stage as potential predictors for pCR. Probability of pCR was studied via logistic regression. Performance of the prediction nomogram was quantified using the concordance statistic (c-statistic) and corrected for optimism.
Results:
A total of 381 patients were included. After surgery, 27.6% of the tumours showed a pCR. Female sex, squamous cell histology, poor differentiation grade, and low cT-stage were predictive for a pCR with a c-statistic of 0.64 (corrected for optimism).
Conclusion:
A nomogram for the prediction of pathologically complete response after neoadjuvant chemoradiotherapy was developed, with a reasonable predictive power. This nomogram needs external validation before it can be used for individualised clinical decision-making.
More Related Videos
03:05Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors
Published on: February 16, 2024
07:13Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
Published on: April 18, 2025